Biclustering reveals breast cancer tumour subgroups with common clinical features and improves prediction of disease recurrence
1 Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
2 Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
3 New Zealand Bioinformatics Institute, University of Auckland, Auckland, New Zealand
4 Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand
5 Department of Engineering Science, University of Auckland, Auckland, New Zealand
6 Melbourne School of Engineering, University of Melbourne, Victoria, Australia
Citation and License
BMC Genomics 2013, 14:102 doi:10.1186/1471-2164-14-102Published: 13 February 2013
Many studies have revealed correlations between breast tumour phenotypes, variations in gene expression, and patient survival outcomes. The molecular heterogeneity between breast tumours revealed by these studies has allowed prediction of prognosis and has underpinned stratified therapy, where groups of patients with particular tumour types receive specific treatments. The molecular tests used to predict prognosis and stratify treatment usually utilise fixed sets of genomic biomarkers, with the same biomarker sets being used to test all patients. In this paper we suggest that instead of fixed sets of genomic biomarkers, it may be more effective to use a stratified biomarker approach, where optimal biomarker sets are automatically chosen for particular patient groups, analogous to the choice of optimal treatments for groups of similar patients in stratified therapy. We illustrate the effectiveness of a biclustering approach to select optimal gene sets for determining the prognosis of specific strata of patients, based on potentially overlapping, non-discrete molecular characteristics of tumours.
Biclustering identified tightly co-expressed gene sets in the tumours of restricted subgroups of breast cancer patients. The co-expressed genes in these biclusters were significantly enriched for particular biological annotations and gene regulatory modules associated with breast cancer biology. Tumours identified within the same bicluster were more likely to present with similar clinical features. Bicluster membership combined with clinical information could predict patient prognosis in conditional inference tree and ridge regression class prediction models.
The increasing clinical use of genomic profiling demands identification of more effective methods to segregate patients into prognostic and treatment groups. We have shown that biclustering can be used to select optimal gene sets for determining the prognosis of specific strata of patients.